New

app.py

@@ -1,67 +1,41 @@
 import os
 import wget
-apex_file = wget.download(os.getenv('apex'))
-os.system('tar zxvf {}'.format(apex_file))
-
-os.system('ls -l; cd apex;' 
-    'pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" .; cd ..')
-
-# os.system('pip install gradio --upgrade')
-
-resources = os.getenv('resources')
-resources_tokenizer = os.getenv('resources_tokenizer')
-
-_ = wget.download(resources_tokenizer)
+resources = os.getenv('resources_new')
 resources_filename = wget.download(resources)
 
 os.system('tar zxvf {}'.format(resources_filename))
-
-# if not os.path.exists('./model_optim_rng.pth'):
-#    resources_weight = os.getenv('resources_weight')
-#    _ = wget.download(resources_weight)
-
 os.system('ls -l')
 
-
-import time
 import argparse
 import datetime
 import json
-import re
 import os
+import time
+import torch
+
 import gradio as gr
 import requests
-from utils import (
-    mplug_owl, load_demo_refresh_model_list, vote_last_response, 
-    upvote_last_response, downvote_last_response, flag_last_response, regenerate, 
-    add_text, after_process_image, get_inputs, init,
-    headers, no_change_btn, enable_btn, disable_btn, get_window_url_params
-)
+
+from conversation import default_conversation
 from gradio_css import code_highlight_css
 from gradio_patch import Chatbot as grChatbot
-from conversation import default_conversation
+from serve_utils import (
+    add_text, after_process_image, disable_btn, no_change_btn,
+    downvote_last_response, enable_btn, flag_last_response,
+    get_window_url_params, init, regenerate, upvote_last_response,
+    after_process_video
+)
+from model_worker import mPLUG_Owl_Server
+from model_utils import post_process_code
 
 SHARED_UI_WARNING = f'''### [NOTE] You can duplicate and use it with a paid private GPU.
 <a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/MAGAer13/mPLUG-Owl?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-md.svg" alt="Duplicate Space"></a>
 '''
 
-# Alternatively, you can also use the Colab demo on our project page.
-# <a style="display:inline-block" href="https://https://github.com/X-PLUG/mPLUG-Owl"><img style="margin-top:0;margin-bottom:0" src="https://img.shields.io/badge/Project%20Page-online-brightgreen"></a>
-
-io = None
-init()
-model = mplug_owl(device="cuda")
-log_dir = ""
 
 def load_demo(url_params, request: gr.Request):
 
     dropdown_update = gr.Dropdown.update(visible=True)
-    if "model" in url_params:
-        model = url_params["model"]
-        if model in models:
-            dropdown_update = gr.Dropdown.update(
-                value=model, visible=True)
-
     state = default_conversation.copy()
 
     return (state,
@@ -72,36 +46,54 @@ def load_demo(url_params, request: gr.Request):
             gr.Row.update(visible=True),
             gr.Accordion.update(visible=True))
 
-def contains_chinese(string):
-    pattern = re.compile(r'[一-龥]')
-    match = pattern.search(string)
-    return match is not None
-
 def clear_history(request: gr.Request):
     state = default_conversation.copy()
 
     return (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn,) * 5
 
-def http_bot(state, topk, max_new_tokens, random_seed, request: gr.Request):
+def http_bot(state, max_output_tokens, temperature, top_k, top_p, 
+            num_beams, no_repeat_ngram_size, length_penalty,
+            do_sample, request: gr.Request):
+    if state.skip_next:
+        # This generate call is skipped due to invalid inputs
+        yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
+        return
+
     prompt = after_process_image(state.get_prompt())
     images = state.get_images()
+
+    data = {
+        "text_input": prompt,
+        "images": images if len(images) > 0 else [],
+        "generation_config": {
+            "top_k": int(top_k),
+            "top_p": float(top_p),
+            "num_beams": int(num_beams),
+            "no_repeat_ngram_size": int(no_repeat_ngram_size),
+            "length_penalty": float(length_penalty),
+            "do_sample": bool(do_sample),
+            "temperature": float(temperature),
+            "max_new_tokens": min(int(max_output_tokens), 1536),
+        }
+    }
+
     state.messages[-1][-1] = "▌"
     yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
 
-    # if contains_chinese(prompt):
-    #     state.messages[-1][-1] = "**CURRENTLY WE ONLY SUPPORT ENGLISH. PLEASE REFRESH THIS PAGE TO RESTART.**"
-    #     yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
-    #     return
-
     try:
-        data = get_inputs(prompt, images, topk, max_new_tokens, random_seed)
-        output = model.prediction(data, log_dir)
-        print(output)
-        # output = output.replace("```", "")
-
-        state.messages[-1][-1] = output + "▌"
-        yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
-        time.sleep(0.03)
+        for chunk in model.predict(data):
+            if chunk:
+                if chunk[1]:
+                    output = chunk[0].strip()
+                    output = post_process_code(output)
+                    state.messages[-1][-1] = output + "▌"
+                    yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
+                else:
+                    output = chunk[0].strip()
+                    state.messages[-1][-1] = output
+                    yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
+                    return
+                time.sleep(0.03)
 
     except requests.exceptions.RequestException as e:
         state.messages[-1][-1] = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**"
@@ -112,25 +104,23 @@ def http_bot(state, topk, max_new_tokens, random_seed, request: gr.Request):
     yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
 
 
-def add_text_http_bot(state, text, image, video, topk, max_new_tokens, random_seed, request: gr.Request):
-    if len(text) <= 0 and (image is None or video is None):
+def add_text_http_bot(
+    state, text, image, video, num_frames,
+    max_output_tokens, temperature, top_k, top_p, 
+    num_beams, no_repeat_ngram_size, length_penalty,
+    do_sample, request: gr.Request):
+    if len(text) <= 0 and image is None and video is None:
         state.skip_next = True
         return (state, state.to_gradio_chatbot(), "", None, None) + (no_change_btn,) * 5
 
     if image is not None:
-        multimodal_msg = None
         if '<image>' not in text:
             text = text + '\n<image>'
-
-        if multimodal_msg is not None:
-            return (state, state.to_gradio_chatbot(), multimodal_msg, None, None) + (
-                no_change_btn,) * 5
         text = (text, image)
     
     if video is not None:
-        num_frames = 4
-        if '<image>' not in text:
-            text = text + '\n<image>' * num_frames
+        if '<|video|>' not in text:
+            text = text + '\n<|video|>'
         text = (text, video)
 
     state.append_message(state.roles[0], text)
@@ -138,26 +128,54 @@ def add_text_http_bot(state, text, image, video, topk, max_new_tokens, random_se
     state.skip_next = False
 
     yield (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn,) * 5
-    
-    prompt = after_process_image(state.get_prompt())
+
+    if state.skip_next:
+        # This generate call is skipped due to invalid inputs
+        yield (state, state.to_gradio_chatbot(), "", None, None) + (no_change_btn,) * 5
+        return
+
+    prompt = state.get_prompt(num_frames)
+    prompt = after_process_image(prompt)
+    prompt = after_process_video(prompt)
+    prompt = prompt.replace("Human: \n", "")
+
     images = state.get_images()
+    videos = state.get_videos(num_frames)
+
+    data = {
+        "text_input": prompt,
+        "images": images if len(images) > 0 else [],
+        "videos": videos if len(videos) > 0 else [],
+        "video": video if video is not None else None,
+        "generation_config": {
+            "top_k": int(top_k),
+            "top_p": float(top_p),
+            "num_beams": int(num_beams),
+            "no_repeat_ngram_size": int(no_repeat_ngram_size),
+            "length_penalty": float(length_penalty),
+            "do_sample": bool(do_sample),
+            "temperature": float(temperature),
+            "max_new_tokens": min(int(max_output_tokens), 1536),
+        }
+    }
+
     state.messages[-1][-1] = "▌"
     yield (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn,) * 5
 
-    # if contains_chinese(prompt):
-    #     state.messages[-1][-1] = "**CURRENTLY WE ONLY SUPPORT ENGLISH. PLEASE REFRESH THIS PAGE TO RESTART OR CLEAR HISTORY.**"
-    #     yield (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
-    #     return
-
     try:
-        data = get_inputs(prompt, images, topk, max_new_tokens, random_seed)
-        output = model.prediction(data, log_dir)
-        print(output)
-        # output = output.replace("```", "")
-
-        state.messages[-1][-1] = output + "▌"
-        yield (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn,) * 5
-        time.sleep(0.03)
+        for chunk in model.predict(data):
+            if chunk:
+                if chunk[1]:
+                    output = chunk[0].strip()
+                    output = post_process_code(output)
+                    state.messages[-1][-1] = output + "▌"
+                    yield (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn,) * 5
+                else:
+                    output = chunk[0].strip()
+                    state.messages[-1][-1] = output
+                    yield (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
+                    return
+                time.sleep(0.03)
 
     except requests.exceptions.RequestException as e:
         state.messages[-1][-1] = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**"
@@ -168,33 +186,53 @@ def add_text_http_bot(state, text, image, video, topk, max_new_tokens, random_se
     yield (state, state.to_gradio_chatbot(), "", None, None) + (enable_btn,) * 5
 
 
-def regenerate_http_bot(state, topk, max_new_tokens, random_seed, request: gr.Request):
+def regenerate_http_bot(state, num_frames,
+    max_output_tokens, temperature, top_k, top_p, 
+    num_beams, no_repeat_ngram_size, length_penalty,
+    do_sample, request: gr.Request):
     state.messages[-1][-1] = None
     state.skip_next = False
     yield (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn,) * 5
-    
-    prompt = after_process_image(state.get_prompt())
+
+    prompt = after_process_image(state.get_prompt(num_frames))
     images = state.get_images()
-    state.messages[-1][-1] = " "
-    yield (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn,) * 5
+    videos = state.get_videos(num_frames)
+
+    data = {
+        "text_input": prompt,
+        "images": images if len(images) > 0 else [],
+        "videos": videos if len(videos) > 0 else [],
+        "generation_config": {
+            "top_k": int(top_k),
+            "top_p": float(top_p),
+            "num_beams": int(num_beams),
+            "no_repeat_ngram_size": int(no_repeat_ngram_size),
+            "length_penalty": float(length_penalty),
+            "do_sample": bool(do_sample),
+            "temperature": float(temperature),
+            "max_new_tokens": min(int(max_output_tokens), 1536),
+        }
+    }
 
-    # if contains_chinese(prompt):
-    #     state.messages[-1][-1] = "**CURRENTLY WE ONLY SUPPORT ENGLISH. PLEASE REFRESH THIS PAGE TO RESTART OR CLEAR HISTORY.**"
-    #     yield (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
-    #     return
+    state.messages[-1][-1] = "▌"
+    yield (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn,) * 5
 
     try:
-        data = get_inputs(prompt, images, topk, max_new_tokens, random_seed)
-        output = model.prediction(data, log_dir)
-        print(">>>> output:", output)
-        # output = output.replace("```", "")
-
-        state.messages[-1][-1] = output + " "
-        yield (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn,) * 5
-        time.sleep(0.03)
+        for chunk in model.predict(data):
+            if chunk:
+                if chunk[1]:
+                    output = chunk[0].strip()
+                    output = post_process_code(output)
+                    state.messages[-1][-1] = output + "▌"
+                    yield (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn,) * 5
+                else:
+                    output = chunk[0].strip()
+                    state.messages[-1][-1] = output
+                    yield (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
+                    return
+                time.sleep(0.03)
 
     except requests.exceptions.RequestException as e:
-        print(e)
         state.messages[-1][-1] = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**"
         yield (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
         return
@@ -202,25 +240,38 @@ def regenerate_http_bot(state, topk, max_new_tokens, random_seed, request: gr.Re
     state.messages[-1][-1] = state.messages[-1][-1][:-1]
     yield (state, state.to_gradio_chatbot(), "", None, None) + (enable_btn,) * 5
 
+# [![Star on GitHub](https://img.shields.io/github/stars/X-PLUG/mPLUG-Owl.svg?style=social)](https://github.com/X-PLUG/mPLUG-Owl/stargazers)
+# **If you are facing ERROR, it might be Out-Of-Memory (OOM) issue due to the limited GPU memory, please refresh the page to restart.** Besides, we recommand you to duplicate the space with a single A10 GPU to have a better experience. Or you can visit our demo hosted on [Modelscope](https://www.modelscope.cn/studios/damo/mPLUG-Owl/summary) which is hosted on a V100 machine.
+
 title_markdown = ("""
-# mPLUG-Owl🦉 (GitHub Repo: https://github.com/X-PLUG/mPLUG-Owl) 
-## If you like our project, please give us a star ✨ on Github for latest update. 
-[![Star on GitHub](https://img.shields.io/github/stars/X-PLUG/mPLUG-Owl.svg?style=social)](https://github.com/X-PLUG/mPLUG-Owl/stargazers)
-""")
+<h1 align="center"><a href="https://github.com/X-PLUG/mPLUG-Owl"><img src="https://s1.ax1x.com/2023/05/12/p9yGA0g.png", alt="mPLUG-Owl" border="0" style="margin: 0 auto; height: 200px;" /></a> </h1>
 
-tos_markdown = ("""
-### Notice
-The output is generated by top-k sampling scheme and may involve some randomness. For multiple image and video, we cannot ensure it's performance since only image-text pairs are used during training. For Video inputs, we recommand use the video **less than 10 seconds**.
+<h2 align="center"> mPLUG-Owl🦉: Modularization Empowers Large Language Models with Multimodality </h2>
 
-**If you are facing ERROR, it might be Out-Of-Memory (OOM) issue due to the limited GPU memory, please refresh the page to restart.** Besides, we recommand you to duplicate the space with a single A10 GPU to have a better experience. Or you can visit our demo hosted on [Modelscope](https://www.modelscope.cn/studios/damo/mPLUG-Owl/summary) which is hosted on a V100 machine.
+<h5 align="center"> If you like our project, please give us a star ✨ on Github for latest update.  </h2>
 
+<div align="center">
+    <div style="display:flex; gap: 0.25rem;" align="center">
+        <a href='https://github.com/X-PLUG/mPLUG-Owl'><img src='https://img.shields.io/badge/Github-Code-blue'></a>
+        <a href="https://arxiv.org/abs/2304.14178"><img src="https://img.shields.io/badge/Arxiv-2304.14178-red"></a>
+        <a href='https://github.com/X-PLUG/mPLUG-Owl/stargazers'><img src='https://img.shields.io/github/stars/X-PLUG/mPLUG-Owl.svg?style=social'></a>
+    </div>
+</div>
+
+**Notice**: The output is generated by top-k sampling scheme and may involve some randomness. For multiple images and video, we cannot ensure it's performance since only image-text / video-text pairs are used during training.
+
+**We recommand only one image or video per conversation session.** If you want to start to chat with new images or videos, we recommand you to **CLEAR** the history to restart.
+
+""")
+
+tos_markdown = ("""
 ### Terms of use
 By using this service, users are required to agree to the following terms:
 The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research.
 Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator.
 For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.
 
-Copyright 2023 Alibaba DAMO Academy.
+**Copyright 2023 Alibaba DAMO Academy.**
 """)
 
 learn_more_markdown = ("""
@@ -229,14 +280,19 @@ The service is a research preview intended for non-commercial use only, subject
 """)
 
 css = code_highlight_css + """
-version 1.0
+pre {
+    white-space: pre-wrap;       /* Since CSS 2.1 */
+    white-space: -moz-pre-wrap;  /* Mozilla, since 1999 */
+    white-space: -pre-wrap;      /* Opera 4-6 */
+    white-space: -o-pre-wrap;    /* Opera 7 */
+    word-wrap: break-word;       /* Internet Explorer 5.5+ */
+}
 """
 
 def build_demo():
     # with gr.Blocks(title="mPLUG-Owl🦉", theme=gr.themes.Base(), css=css) as demo:
     with gr.Blocks(title="mPLUG-Owl🦉", css=css) as demo:
         state = gr.State()
-
         gr.Markdown(SHARED_UI_WARNING)
 
         gr.Markdown(title_markdown)
@@ -245,17 +301,23 @@ def build_demo():
             with gr.Column(scale=3):
 
                 imagebox = gr.Image(type="pil")
-
                 videobox = gr.Video()
 
                 with gr.Accordion("Parameters", open=True, visible=False) as parameter_row:
-                    topk = gr.Slider(minimum=1, maximum=5, value=5, step=1, interactive=True, label="Top K",)
                     max_output_tokens = gr.Slider(minimum=0, maximum=1024, value=512, step=64, interactive=True, label="Max output tokens",)
-                    temperature = gr.Slider(minimum=0, maximum=10, value=1, step=0.1, interactive=True, label="Temperature",)
+                    temperature = gr.Slider(minimum=0, maximum=1, value=1, step=0.1, interactive=True, label="Temperature",)
+                    top_k = gr.Slider(minimum=1, maximum=5, value=3, step=1, interactive=True, label="Top K",)
+                    top_p = gr.Slider(minimum=0, maximum=1, value=0.9, step=0.1, interactive=True, label="Top p",)
+                    length_penalty = gr.Slider(minimum=1, maximum=5, value=1, step=0.1, interactive=True, label="length_penalty",)
+                    num_beams = gr.Slider(minimum=1, maximum=5, value=1, step=1, interactive=True, label="Beam Size",)
+                    no_repeat_ngram_size = gr.Slider(minimum=1, maximum=5, value=2, step=1, interactive=True, label="no_repeat_ngram_size",)
+                    num_frames = gr.Slider(minimum=8, maximum=32, value=8, step=4, interactive=True, label="Number of Frames",)
+                    do_sample = gr.Checkbox(interactive=True, value=True, label="do_sample")
+
                 gr.Markdown(tos_markdown)
 
             with gr.Column(scale=6):
-                chatbot = grChatbot(elem_id="chatbot", visible=False).style(height=800)
+                chatbot = grChatbot(elem_id="chatbot", visible=False).style(height=1000)
                 with gr.Row():
                     with gr.Column(scale=8):
                         textbox = gr.Textbox(show_label=False,
@@ -278,18 +340,17 @@ def build_demo():
             [f'examples/laundry.jpeg', 'Why this happens and how to fix it?'],
             [f'examples/ca.jpeg', "What do you think about the person's behavior?"],
             [f'examples/monalisa-fun.jpg', 'Do you know who drew this painting?​'],
-            # [f"examples/Yao_Ming.jpeg", "What is the name of the man on the right?"],
         ], inputs=[imagebox, textbox])
 
-        gr.Examples(examples=[
-            [f"examples/surf.mp4", "What is the man doing?"],
-            [f"examples/yoga.mp4", "What did the woman doing?"],
-        ], inputs=[videobox, textbox])
-
         gr.Markdown(learn_more_markdown)
         url_params = gr.JSON(visible=False)
 
         btn_list = [upvote_btn, downvote_btn, flag_btn, regenerate_btn, clear_btn]
+        parameter_list = [
+            num_frames, max_output_tokens, temperature, top_k, top_p, 
+            num_beams, no_repeat_ngram_size, length_penalty,
+            do_sample
+        ]
         upvote_btn.click(upvote_last_response,
             [state], [textbox, upvote_btn, downvote_btn, flag_btn])
         downvote_btn.click(downvote_last_response,
@@ -297,19 +358,30 @@ def build_demo():
         flag_btn.click(flag_last_response,
             [state], [textbox, upvote_btn, downvote_btn, flag_btn])
         # regenerate_btn.click(regenerate, state,
-        #     [state, chatbot, textbox, imagebox] + btn_list).then(
-        #     http_bot, [state, topk, max_output_tokens, temperature],
+        #     [state, chatbot, textbox, imagebox, videobox] + btn_list).then(
+        #     http_bot, [state] + parameter_list,
         #     [state, chatbot] + btn_list)
-        regenerate_btn.click(regenerate_http_bot, [state, topk, max_output_tokens, temperature],
+        regenerate_btn.click(regenerate_http_bot, [state] + parameter_list,
             [state, chatbot, textbox, imagebox, videobox] + btn_list)
+
         clear_btn.click(clear_history, None, [state, chatbot, textbox, imagebox, videobox] + btn_list)
 
-        # textbox.submit(add_text, [state, textbox, imagebox], [state, chatbot, textbox, imagebox] + btn_list
-        #     ).then(http_bot, [state, topk, max_output_tokens, temperature],
+        # textbox.submit(add_text, [state, textbox, imagebox, videobox], [state, chatbot, textbox, imagebox, videobox] + btn_list
+        #     ).then(http_bot, [state] + parameter_list,
         #            [state, chatbot] + btn_list)
-        textbox.submit(add_text_http_bot, [state, textbox, imagebox, videobox, topk, max_output_tokens, temperature], [state, chatbot, textbox, imagebox, videobox] + btn_list)
+        # submit_btn.click(add_text, [state, textbox, imagebox, videobox], [state, chatbot, textbox, imagebox, videobox] + btn_list
+        #     ).then(http_bot, [state] + parameter_list,
+        #            [state, chatbot] + btn_list)
+
+        textbox.submit(add_text_http_bot, 
+            [state, textbox, imagebox, videobox] + parameter_list, 
+            [state, chatbot, textbox, imagebox, videobox] + btn_list
+        )
 
-        submit_btn.click(add_text_http_bot, [state, textbox, imagebox, videobox, topk, max_output_tokens, temperature], [state, chatbot, textbox, imagebox, videobox] + btn_list)
+        submit_btn.click(add_text_http_bot, 
+            [state, textbox, imagebox, videobox] + parameter_list, 
+            [state, chatbot, textbox, imagebox, videobox] + btn_list
+        )
 
         demo.load(load_demo, [url_params], [state,
             chatbot, textbox, submit_btn, button_row, parameter_row],
@@ -318,15 +390,30 @@ def build_demo():
     return demo
 
 if __name__ == "__main__":
-    cur_dir = os.getenv("cur_dir")
-    log_dir = cur_dir + "log"
+    io = init()
 
     parser = argparse.ArgumentParser()
     parser.add_argument("--host", type=str, default="0.0.0.0")
     parser.add_argument("--debug", action="store_true", help="using debug mode")
     parser.add_argument("--port", type=int)
     parser.add_argument("--concurrency-count", type=int, default=1)
+    parser.add_argument("--base-model",type=str, default='./')
+    parser.add_argument("--load-8bit", action="store_true", help="using 8bit mode")
+    parser.add_argument("--bf16", action="store_true", default=True, help="using 8bit mode")
     args = parser.parse_args()
+
+    if torch.cuda.is_available():
+        device = "cuda"
+    else:
+        device = "cpu"
+
+    model = mPLUG_Owl_Server(
+        base_model=args.base_model,
+        load_in_8bit=args.load_8bit,
+        bf16=args.bf16,
+        device=device,
+        io=io
+    )
     demo = build_demo()
     demo.queue(concurrency_count=args.concurrency_count, status_update_rate=10, api_open=False).launch(server_name=args.host, debug=args.debug, server_port=args.port, share=False)
 

config.json

@@ -0,0 +1,299 @@
+{
+    "return_dict": true,
+    "output_hidden_states": false,
+    "output_attentions": false,
+    "torchscript": false,
+    "torch_dtype": null,
+    "use_bfloat16": false,
+    "tf_legacy_loss": false,
+    "pruned_heads": {},
+    "tie_word_embeddings": false,
+    "is_encoder_decoder": false,
+    "is_decoder": false,
+    "cross_attention_hidden_size": null,
+    "add_cross_attention": false,
+    "tie_encoder_decoder": false,
+    "max_length": 20,
+    "min_length": 0,
+    "do_sample": false,
+    "early_stopping": false,
+    "num_beams": 1,
+    "num_beam_groups": 1,
+    "diversity_penalty": 0.0,
+    "temperature": 1.0,
+    "top_k": 50,
+    "top_p": 1.0,
+    "typical_p": 1.0,
+    "repetition_penalty": 1.0,
+    "length_penalty": 1.0,
+    "no_repeat_ngram_size": 0,
+    "encoder_no_repeat_ngram_size": 0,
+    "bad_words_ids": null,
+    "num_return_sequences": 1,
+    "chunk_size_feed_forward": 0,
+    "output_scores": false,
+    "return_dict_in_generate": false,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "remove_invalid_values": false,
+    "exponential_decay_length_penalty": null,
+    "suppress_tokens": null,
+    "begin_suppress_tokens": null,
+    "architectures": null,
+    "finetuning_task": null,
+    "id2label": {
+        "0": "LABEL_0",
+        "1": "LABEL_1"
+    },
+    "label2id": {
+        "LABEL_0": 0,
+        "LABEL_1": 1
+    },
+    "tokenizer_class": null,
+    "prefix": null,
+    "bos_token_id": null,
+    "pad_token_id": null,
+    "eos_token_id": null,
+    "sep_token_id": null,
+    "decoder_start_token_id": null,
+    "task_specific_params": null,
+    "problem_type": null,
+    "_name_or_path": "",
+    "_commit_hash": null,
+    "transformers_version": null,
+    "vision_config": {
+        "return_dict": true,
+        "output_hidden_states": false,
+        "output_attentions": false,
+        "torchscript": false,
+        "torch_dtype": null,
+        "use_bfloat16": false,
+        "tf_legacy_loss": false,
+        "pruned_heads": {},
+        "tie_word_embeddings": true,
+        "is_encoder_decoder": false,
+        "is_decoder": false,
+        "cross_attention_hidden_size": null,
+        "add_cross_attention": false,
+        "tie_encoder_decoder": false,
+        "max_length": 20,
+        "min_length": 0,
+        "do_sample": false,
+        "early_stopping": false,
+        "num_beams": 1,
+        "num_beam_groups": 1,
+        "diversity_penalty": 0.0,
+        "temperature": 1.0,
+        "top_k": 50,
+        "top_p": 1.0,
+        "typical_p": 1.0,
+        "repetition_penalty": 1.0,
+        "length_penalty": 1.0,
+        "no_repeat_ngram_size": 0,
+        "encoder_no_repeat_ngram_size": 0,
+        "bad_words_ids": null,
+        "num_return_sequences": 1,
+        "chunk_size_feed_forward": 0,
+        "output_scores": false,
+        "return_dict_in_generate": false,
+        "forced_bos_token_id": null,
+        "forced_eos_token_id": null,
+        "remove_invalid_values": false,
+        "exponential_decay_length_penalty": null,
+        "suppress_tokens": null,
+        "begin_suppress_tokens": null,
+        "architectures": null,
+        "finetuning_task": null,
+        "id2label": {
+            "0": "LABEL_0",
+            "1": "LABEL_1"
+        },
+        "label2id": {
+            "LABEL_0": 0,
+            "LABEL_1": 1
+        },
+        "tokenizer_class": null,
+        "prefix": null,
+        "bos_token_id": null,
+        "pad_token_id": null,
+        "eos_token_id": null,
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+        "decoder_start_token_id": null,
+        "task_specific_params": null,
+        "problem_type": null,
+        "_name_or_path": "",
+        "transformers_version": "4.29.0.dev0",
+        "model_type": "mplug_owl_vision_model",
+        "hidden_size": 1024,
+        "intermediate_size": 4096,
+        "projection_dim": 768,
+        "num_hidden_layers": 24,
+        "num_attention_heads": 16,
+        "num_channels": 3,
+        "patch_size": 14,
+        "image_size": 224,
+        "initializer_range": 0.02,
+        "initializer_factor": 1.0,
+        "attention_dropout": 0.0,
+        "layer_norm_eps": 1e-06,
+        "hidden_act": "quick_gelu"
+    },
+    "visual_abstractor_config": {
+        "return_dict": true,
+        "output_hidden_states": false,
+        "output_attentions": false,
+        "torchscript": false,
+        "torch_dtype": null,
+        "use_bfloat16": false,
+        "tf_legacy_loss": false,
+        "pruned_heads": {},
+        "tie_word_embeddings": true,
+        "is_encoder_decoder": false,
+        "is_decoder": false,
+        "cross_attention_hidden_size": null,
+        "add_cross_attention": false,
+        "tie_encoder_decoder": false,
+        "max_length": 20,
+        "min_length": 0,
+        "do_sample": false,
+        "early_stopping": false,
+        "num_beams": 1,
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+        "diversity_penalty": 0.0,
+        "temperature": 1.0,
+        "top_k": 50,
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+        "output_scores": false,
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+        "architectures": null,
+        "finetuning_task": null,
+        "id2label": {
+            "0": "LABEL_0",
+            "1": "LABEL_1"
+        },
+        "label2id": {
+            "LABEL_0": 0,
+            "LABEL_1": 1
+        },
+        "tokenizer_class": null,
+        "prefix": null,
+        "bos_token_id": null,
+        "pad_token_id": 0,
+        "eos_token_id": null,
+        "sep_token_id": null,
+        "decoder_start_token_id": null,
+        "task_specific_params": null,
+        "problem_type": null,
+        "_name_or_path": "",
+        "transformers_version": "4.29.0.dev0",
+        "vocab_size": 30522,
+        "hidden_size": 1024,
+        "num_hidden_layers": 6,
+        "num_attention_heads": 16,
+        "hidden_act": "gelu",
+        "intermediate_size": 4096,
+        "hidden_dropout_prob": 0.1,
+        "attention_probs_dropout_prob": 0.1,
+        "max_position_embeddings": 512,
+        "initializer_range": 0.02,
+        "layer_norm_eps": 1e-06,
+        "position_embedding_type": "absolute",
+        "classifier_dropout": null,
+        "cross_attention_frequency": 2,
+        "encoder_hidden_size": 1024,
+        "model_type": "MPlugOwlVisualAbstractor"
+    },
+    "text_config": {
+        "vocab_size": 32000,
+        "max_position_embeddings": 2048,
+        "hidden_size": 4096,
+        "intermediate_size": 11008,
+        "num_hidden_layers": 32,
+        "num_attention_heads": 32,
+        "hidden_act": "silu",
+        "initializer_range": 0.02,
+        "rms_norm_eps": 1e-06,
+        "use_cache": true,
+        "return_dict": true,
+        "output_hidden_states": false,
+        "output_attentions": false,
+        "torchscript": false,
+        "torch_dtype": null,
+        "use_bfloat16": false,
+        "tf_legacy_loss": false,
+        "pruned_heads": {},
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+        "is_encoder_decoder": false,
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+        "early_stopping": false,
+        "num_beams": 1,
+        "num_beam_groups": 1,
+        "diversity_penalty": 0.0,
+        "temperature": 1.0,
+        "top_k": 50,
+        "top_p": 1.0,
+        "typical_p": 1.0,
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+        "bad_words_ids": null,
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+        "suppress_tokens": null,
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+        "architectures": null,
+        "finetuning_task": null,
+        "id2label": {
+            "0": "LABEL_0",
+            "1": "LABEL_1"
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+            "LABEL_0": 0,
+            "LABEL_1": 1
+        },
+        "tokenizer_class": null,
+        "prefix": null,
+        "bos_token_id": 1,
+        "pad_token_id": 2,
+        "eos_token_id": 2,
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+        "problem_type": null,
+        "_name_or_path": "",
+        "transformers_version": "4.29.0.dev0",
+        "model_type": "llama"
+    },
+    "num_query_tokens": 64,
+    "use_decoder_only_language_model": true,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "model_type": "mplug-owl"
+}

preprocessor_config.json

@@ -0,0 +1,9 @@
+{
+    "resolution": 224,
+    "image_mean": [
+        0.48145466, 0.4578275, 0.40821073
+    ],
+    "image_std": [
+        0.26862954, 0.26130258, 0.27577711
+    ]
+}

model_optim_rng.pt → pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:af244914525f592ad44647084b05b02e49c12e6d42e91232bbe6cd7aeff0ed17
-size 14305364484
+oid sha256:5930c4a575f679aaef82b3c4c4601f3c57bafcc54c4f19431f7228267084968e
+size 14305584703

tokenizer_config.json

@@ -0,0 +1 @@
+{"bos_token": "<s>", "eos_token": "</s>", "model_max_length": 1000000000000000019884624838656, "tokenizer_class": "MplugOwlTokenizer", "unk_token": "<unk>", "pad_token": "<unk>"}